Machine Learning Training by Experts
Our Training Process

Machine Learning - Syllabus, Fees & Duration
Module 1 : CORE PYTHON
- Short history
 - Introduction
 - Features of Python
 - Python2 Vs Python 3
 - Python Installation
 - Python Interpreter
 - How to Run Python
 - Basic Syntax
 - Python Identifiers, Keywords and Indentation Rules
 - Type Checking
 - Input, Output, Variables, Data Type and Datatype Casting
 
Module 2 : MACHINE LEARNING
- Data Analysis
 - Data Visualization
 - Data Classification
- Supervised Learning Unsupervised Learning
 
 
Module 3 : SUPERVISED LEARNING
-  Classification
 
- K-Nearest Neighbours
 - Decision Tree
 - Naive Bayes
 - Logistic Regression
 - Support Vector Machine
 - Random Forest
 - Logistic Regression
 
 -  Regression
- Single Linear Regression
 - Multiple Linear Regression
 
 
Module 4 : UNSUPERVISED LEARNING
-  Clustering
- Hierarchical Clustering
 - KMeans Algorithm Association
 
 
Module 5 : DATA PREPROCESSING
- PCA
 - Dimensionality reduction
 - Correlation
 - Features Extraction Algorithm
 
This syllabus is not final and can be customized as per needs/updates
			
													
												
							

								
							
			 Learning machine learning can help you advance your profession.  Machine learning is the most in-demand position in the information technology industry right now. 
.  Check out our NESTSOFT courses in Victoria if you're interested in learning more about Machine Learning.  Anyone who completes this course can expect a typical salary increase of 48% and be hired by one of our 100+ hiring employers.  The student will be able to create and apply pattern classification algorithms to categorize multivariate data, create and apply regression algorithms to uncover correlations between data variables, and use reinforcement learning methods to operate complicated systems after finishing the course.  
An overview of artificial intelligence and machine learning, fundamental principles for machine learning, data pre-processing, feature extraction, regression, logistic regression, nave Bayes, decision trees, kernel methods, and support vector machine and k-means and hierarchical clustering are among the topics covered in this course.  We live in a world surrounded by humans who can study everything using their abilities and learning abilities, as well as machines that follow our directions.  As a result of the increased demand, experts have been able to land the highest-paying positions.  You'll need data training capabilities, algorithm basics, advanced, automation, and iterative processes, ensemble modeling, and scalability to build a strong ML (machine learning) system.